Search Results for author: Ming Jin

Found 58 papers, 21 papers with code

Skin-in-the-Game: Decision Making via Multi-Stakeholder Alignment in LLMs

no code implementations21 May 2024 Bilgehan Sel, Priya Shanmugasundaram, Mohammad Kachuee, Kun Zhou, Ruoxi Jia, Ming Jin

Large Language Models (LLMs) have shown remarkable capabilities in tasks such as summarization, arithmetic reasoning, and question answering.

Preparing for Black Swans: The Antifragility Imperative for Machine Learning

no code implementations18 May 2024 Ming Jin

Operating safely and reliably despite continual distribution shifts is vital for high-stakes machine learning applications.

Continual Learning Decision Making +3

A Survey on Diffusion Models for Time Series and Spatio-Temporal Data

1 code implementation29 Apr 2024 Yiyuan Yang, Ming Jin, Haomin Wen, Chaoli Zhang, Yuxuan Liang, Lintao Ma, Yi Wang, Chenghao Liu, Bin Yang, Zenglin Xu, Jiang Bian, Shirui Pan, Qingsong Wen

Conditioned models, on the other hand, utilize extra information to enhance performance and are similarly divided for both predictive and generative tasks.

Anomaly Detection Imputation +1

Foundation Models for Time Series Analysis: A Tutorial and Survey

no code implementations21 Mar 2024 Yuxuan Liang, Haomin Wen, Yuqi Nie, Yushan Jiang, Ming Jin, Dongjin Song, Shirui Pan, Qingsong Wen

Time series analysis stands as a focal point within the data mining community, serving as a cornerstone for extracting valuable insights crucial to a myriad of real-world applications.

Time Series Time Series Analysis

Joint Optimization for Achieving Covertness in MIMO Over-the-Air Computation Networks

no code implementations15 Mar 2024 Junteng Yao, Tuo Wu, Ming Jin, Cunhua Pan, Quanzhong Li, Jinhong Yuan

This paper investigates covert data transmission within a multiple-input multiple-output (MIMO) over-the-air computation (AirComp) network, where sensors transmit data to the access point (AP) while guaranteeing covertness to the warden (Willie).

TeaMs-RL: Teaching LLMs to Teach Themselves Better Instructions via Reinforcement Learning

no code implementations13 Mar 2024 Shangding Gu, Alois Knoll, Ming Jin

The development of Large Language Models (LLMs) often confronts challenges stemming from the heavy reliance on human annotators in the reinforcement learning with human feedback (RLHF) framework, or the frequent and costly external queries tied to the self-instruct paradigm.

reinforcement-learning Reinforcement Learning (RL)

Exploring Fairness for FAS-assisted Communication Systems: from NOMA to OMA

no code implementations1 Mar 2024 Junteng Yao, Liaoshi Zhou, Tuo Wu, Ming Jin, Cunhua Pan, Maged Elkashlan, Kai-Kit Wong

This paper addresses the fairness issue within fluid antenna system (FAS)-assisted non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA) systems, where a single fixed-antenna base station (BS) transmits superposition-coded signals to two users, each with a single fluid antenna.

Fairness

Attractor Memory for Long-Term Time Series Forecasting: A Chaos Perspective

no code implementations18 Feb 2024 Jiaxi Hu, Yuehong Hu, Wei Chen, Ming Jin, Shirui Pan, Qingsong Wen, Yuxuan Liang

In long-term time series forecasting (LTSF) tasks, existing deep learning models overlook the crucial characteristic that discrete time series originate from underlying continuous dynamic systems, resulting in a lack of extrapolation and evolution capabilities.

Time Series Time Series Forecasting

The Mirrored Influence Hypothesis: Efficient Data Influence Estimation by Harnessing Forward Passes

no code implementations14 Feb 2024 Myeongseob Ko, Feiyang Kang, Weiyan Shi, Ming Jin, Zhou Yu, Ruoxi Jia

Inspired by this, we introduce a new method for estimating the influence of training data, which requires calculating gradients for specific test samples, paired with a forward pass for each training point.

Memorization

Graph Spatiotemporal Process for Multivariate Time Series Anomaly Detection with Missing Values

no code implementations11 Jan 2024 Yu Zheng, Huan Yee Koh, Ming Jin, Lianhua Chi, Haishuai Wang, Khoa T. Phan, Yi-Ping Phoebe Chen, Shirui Pan, Wei Xiang

However, real-world time series data is usually not well-structured, posting significant challenges to existing approaches: (1) The existence of missing values in multivariate time series data along variable and time dimensions hinders the effective modeling of interwoven spatial and temporal dependencies, resulting in important patterns being overlooked during model training; (2) Anomaly scoring with irregularly-sampled observations is less explored, making it difficult to use existing detectors for multivariate series without fully-observed values.

Anomaly Detection Time Series +1

Improving Intrusion Detection with Domain-Invariant Representation Learning in Latent Space

no code implementations28 Dec 2023 Padmaksha Roy, Tyler Cody, Himanshu Singhal, Kevin Choi, Ming Jin

Domain generalization focuses on leveraging knowledge from multiple related domains with ample training data and labels to enhance inference on unseen in-distribution (IN) and out-of-distribution (OOD) domains.

Domain Generalization Intrusion Detection +2

Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook

5 code implementations16 Oct 2023 Ming Jin, Qingsong Wen, Yuxuan Liang, Chaoli Zhang, Siqiao Xue, Xue Wang, James Zhang, Yi Wang, Haifeng Chen, XiaoLi Li, Shirui Pan, Vincent S. Tseng, Yu Zheng, Lei Chen, Hui Xiong

In this survey, we offer a comprehensive and up-to-date review of large models tailored (or adapted) for time series and spatio-temporal data, spanning four key facets: data types, model categories, model scopes, and application areas/tasks.

Time Series Time Series Analysis

Proactive Monitoring via Jamming in Fluid Antenna Systems

no code implementations11 Oct 2023 Junteng Yao, Tuo Wu, Xiazhi Lai, Ming Jin, Cunhua Pan, Maged Elkashlan, Kai-Kit Wong

Our objective is to maximize the average monitoring rate, whose expression involves the integral of the first-order Marcum $Q$ function.

Position

Practical Membership Inference Attacks Against Large-Scale Multi-Modal Models: A Pilot Study

1 code implementation ICCV 2023 Myeongseob Ko, Ming Jin, Chenguang Wang, Ruoxi Jia

Furthermore, our enhanced attacks outperform the baseline across multiple models and datasets, with the weakly supervised attack demonstrating an average-case performance improvement of $17\%$ and being at least $7$X more effective at low false-positive rates.

Message Passing Based Block Sparse Signal Recovery for DOA Estimation Using Large Arrays

no code implementations1 Sep 2023 Yiwen Mao, Dawei Gao, Qinghua Guo, Ming Jin

This work deals with directional of arrival (DOA) estimation with a large antenna array.

Algorithm of Thoughts: Enhancing Exploration of Ideas in Large Language Models

no code implementations20 Aug 2023 Bilgehan Sel, Ahmad Al-Tawaha, Vanshaj Khattar, Ruoxi Jia, Ming Jin

Current literature, aiming to surpass the "Chain-of-Thought" approach, often resorts to an external modus operandi involving halting, modifying, and then resuming the generation process to boost Large Language Models' (LLMs) reasoning capacities.

In-Context Learning

A Human-on-the-Loop Optimization Autoformalism Approach for Sustainability

no code implementations20 Aug 2023 Ming Jin, Bilgehan Sel, Fnu Hardeep, Wotao Yin

This paper outlines a natural conversational approach to solving personalized energy-related problems using large language models (LLMs).

Correlation-aware Spatial-Temporal Graph Learning for Multivariate Time-series Anomaly Detection

1 code implementation17 Jul 2023 Yu Zheng, Huan Yee Koh, Ming Jin, Lianhua Chi, Khoa T. Phan, Shirui Pan, Yi-Ping Phoebe Chen, Wei Xiang

To overcome these limitations, we propose a novel method, correlation-aware spatial-temporal graph learning (termed CST-GL), for time series anomaly detection.

Anomaly Detection Graph Learning +2

A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection

1 code implementation7 Jul 2023 Ming Jin, Huan Yee Koh, Qingsong Wen, Daniele Zambon, Cesare Alippi, Geoffrey I. Webb, Irwin King, Shirui Pan

In this survey, we provide a comprehensive review of graph neural networks for time series analysis (GNN4TS), encompassing four fundamental dimensions: forecasting, classification, anomaly detection, and imputation.

Anomaly Detection Imputation +2

Towards Complex Dynamic Physics System Simulation with Graph Neural ODEs

no code implementations21 May 2023 Guangsi Shi, Daokun Zhang, Ming Jin, Shirui Pan, Philip S. Yu

To better comprehend the complex physical laws, this paper proposes a novel learning based simulation model- Graph Networks with Spatial-Temporal neural Ordinary Equations (GNSTODE)- that characterizes the varying spatial and temporal dependencies in particle systems using a united end-to-end framework.

LAVA: Data Valuation without Pre-Specified Learning Algorithms

1 code implementation28 Apr 2023 Hoang Anh Just, Feiyang Kang, Jiachen T. Wang, Yi Zeng, Myeongseob Ko, Ming Jin, Ruoxi Jia

(1) We develop a proxy for the validation performance associated with a training set based on a non-conventional class-wise Wasserstein distance between training and validation sets.

Data Valuation

Geometric Relational Embeddings: A Survey

no code implementations24 Apr 2023 Bo Xiong, Mojtaba Nayyeri, Ming Jin, Yunjie He, Michael Cochez, Shirui Pan, Steffen Staab

Geometric relational embeddings map relational data as geometric objects that combine vector information suitable for machine learning and structured/relational information for structured/relational reasoning, typically in low dimensions.

Hierarchical Multi-label Classification Knowledge Graph Completion +1

Monte Carlo Grid Dynamic Programming: Almost Sure Convergence and Probability Constraints

1 code implementation10 Mar 2023 Mohammad S. Ramadan, Ahmad Al-Tawaha, Mohamed Shouman, Ahmed Atallah, Ming Jin

This paper presents a Monte Carlo-based sampling approach for the state space and an interpolation procedure for the resulting value function, dependent on the process noise density, in a "self-approximating" fashion, eliminating the need for ordering or set-membership tests.

Winning the CityLearn Challenge: Adaptive Optimization with Evolutionary Search under Trajectory-based Guidance

no code implementations4 Dec 2022 Vanshaj Khattar, Ming Jin

Modern power systems will have to face difficult challenges in the years to come: frequent blackouts in urban areas caused by high power demand peaks, grid instability exacerbated by intermittent renewable generation, and global climate change amplified by rising carbon emissions.

Decision Making Reinforcement Learning (RL)

On Solution Functions of Optimization: Universal Approximation and Covering Number Bounds

no code implementations2 Dec 2022 Ming Jin, Vanshaj Khattar, Harshal Kaushik, Bilgehan Sel, Ruoxi Jia

We study the expressibility and learnability of convex optimization solution functions and their multi-layer architectural extension.

Non-stationary Risk-sensitive Reinforcement Learning: Near-optimal Dynamic Regret, Adaptive Detection, and Separation Design

no code implementations19 Nov 2022 Yuhao Ding, Ming Jin, Javad Lavaei

We study risk-sensitive reinforcement learning (RL) based on an entropic risk measure in episodic non-stationary Markov decision processes (MDPs).

Reinforcement Learning (RL)

Hyper-Parameter Auto-Tuning for Sparse Bayesian Learning

no code implementations9 Nov 2022 Dawei Gao, Qinghua Guo, Ming Jin, Guisheng Liao, Yonina C. Eldar

Choosing the values of hyper-parameters in sparse Bayesian learning (SBL) can significantly impact performance.

Recognizing Nested Entities from Flat Supervision: A New NER Subtask, Feasibility and Challenges

no code implementations1 Nov 2022 Enwei Zhu, Yiyang Liu, Ming Jin, Jinpeng Li

However, existing nested NER models heavily rely on training data annotated with nested entities, while labeling such data is costly.

named-entity-recognition Named Entity Recognition +1

Variational Bayesian Inference Clustering Based Joint User Activity and Data Detection for Grant-Free Random Access in mMTC

no code implementations25 Oct 2022 Zhaoji Zhang, Qinghua Guo, Ying Li, Ming Jin, Chongwen Huang

Furthermore, in conjunction with the AMP algorithm, a variational Bayesian inference based clustering (VBIC) algorithm is developed to solve this clustering problem.

Bayesian Inference Clustering +1

Learning Neural Networks under Input-Output Specifications

no code implementations23 Feb 2022 Zain ul Abdeen, He Yin, Vassilis Kekatos, Ming Jin

In this paper, we examine an important problem of learning neural networks that certifiably meet certain specifications on input-output behaviors.

Multivariate Time Series Forecasting with Dynamic Graph Neural ODEs

1 code implementation17 Feb 2022 Ming Jin, Yu Zheng, Yuan-Fang Li, Siheng Chen, Bin Yang, Shirui Pan

Multivariate time series forecasting has long received significant attention in real-world applications, such as energy consumption and traffic prediction.

Multivariate Time Series Forecasting Time Series +1

Towards Graph Self-Supervised Learning with Contrastive Adjusted Zooming

no code implementations20 Nov 2021 Yizhen Zheng, Ming Jin, Shirui Pan, Yuan-Fang Li, Hao Peng, Ming Li, Zhao Li

To overcome the aforementioned problems, we introduce a novel self-supervised graph representation learning algorithm via Graph Contrastive Adjusted Zooming, namely G-Zoom, to learn node representations by leveraging the proposed adjusted zooming scheme.

Contrastive Learning Graph Representation Learning +1

Adversarial Unlearning of Backdoors via Implicit Hypergradient

3 code implementations ICLR 2022 Yi Zeng, Si Chen, Won Park, Z. Morley Mao, Ming Jin, Ruoxi Jia

Particularly, its performance is more robust to the variation on triggers, attack settings, poison ratio, and clean data size.

Towards General Robustness to Bad Training Data

no code implementations29 Sep 2021 Tianhao Wang, Yi Zeng, Ming Jin, Ruoxi Jia

In this paper, we focus on the problem of identifying bad training data when the underlying cause is unknown in advance.

Data Summarization

Spatiotemporal Representation Learning on Time Series with Dynamic Graph ODEs

no code implementations29 Sep 2021 Ming Jin, Yuan-Fang Li, Yu Zheng, Bin Yang, Shirui Pan

Spatiotemporal representation learning on multivariate time series has received tremendous attention in forecasting traffic and energy data.

Graph structure learning Representation Learning +2

Recurrent Neural Network Controllers Synthesis with Stability Guarantees for Partially Observed Systems

1 code implementation8 Sep 2021 Fangda Gu, He Yin, Laurent El Ghaoui, Murat Arcak, Peter Seiler, Ming Jin

Neural network controllers have become popular in control tasks thanks to their flexibility and expressivity.

LEMMA

Generative and Contrastive Self-Supervised Learning for Graph Anomaly Detection

1 code implementation23 Aug 2021 Yu Zheng, Ming Jin, Yixin Liu, Lianhua Chi, Khoa T. Phan, Yi-Ping Phoebe Chen

While the generative attribute regression module allows us to capture the anomalies in the attribute space, the multi-view contrastive learning module can exploit richer structure information from multiple subgraphs, thus abling to capture the anomalies in the structure space, mixing of structure, and attribute information.

Attribute Contrastive Learning +3

MS-MDA: Multisource Marginal Distribution Adaptation for Cross-subject and Cross-session EEG Emotion Recognition

1 code implementation16 Jul 2021 Hao Chen, Ming Jin, Zhunan Li, Cunhang Fan, Jinpeng Li, Huiguang He

Although several studies have adopted domain adaptation (DA) approaches to tackle this problem, most of them treat multiple EEG data from different subjects and sessions together as a single source domain for transfer, which either fails to satisfy the assumption of domain adaptation that the source has a certain marginal distribution, or increases the difficulty of adaptation.

Domain Adaptation EEG +2

A Unified Framework for Task-Driven Data Quality Management

no code implementations10 Jun 2021 Tianhao Wang, Yi Zeng, Ming Jin, Ruoxi Jia

High-quality data is critical to train performant Machine Learning (ML) models, highlighting the importance of Data Quality Management (DQM).

Data Summarization Data Valuation +1

Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning

1 code implementation12 May 2021 Ming Jin, Yizhen Zheng, Yuan-Fang Li, Chen Gong, Chuan Zhou, Shirui Pan

To overcome this problem, inspired by the recent success of graph contrastive learning and Siamese networks in visual representation learning, we propose a novel self-supervised approach in this paper to learn node representations by enhancing Siamese self-distillation with multi-scale contrastive learning.

Contrastive Learning Graph Representation Learning

Controlling Smart Inverters using Proxies: A Chance-Constrained DNN-based Approach

no code implementations2 May 2021 Sarthak Gupta, Vassilis Kekatos, Ming Jin

The trained DNNs can be driven by partial, noisy, or proxy descriptors of the current grid conditions.

Unitary Approximate Message Passing for Sparse Bayesian Learning

no code implementations25 Jan 2021 Man Luo, Qinghua Guo, Ming Jin, Yonina C. Eldar, Defeng, Huang, Xiangming Meng

Sparse Bayesian learning (SBL) can be implemented with low complexity based on the approximate message passing (AMP) algorithm.

Variational Inference

Imitation Learning with Stability and Safety Guarantees

1 code implementation16 Dec 2020 He Yin, Peter Seiler, Ming Jin, Murat Arcak

A method is presented to learn neural network (NN) controllers with stability and safety guarantees through imitation learning (IL).

Imitation Learning

Power up! Robust Graph Convolutional Network based on Graph Powering

no code implementations25 Sep 2019 Ming Jin, Heng Chang, Wenwu Zhu, Somayeh Sojoudi

By challenging the classical graph Laplacian, we propose a new convolution operator that is provably robust in the spectral domain and is incorporated in the GCN architecture to improve expressivity and interpretability.

Adversarial Robustness

Power up! Robust Graph Convolutional Network via Graph Powering

1 code implementation24 May 2019 Ming Jin, Heng Chang, Wenwu Zhu, Somayeh Sojoudi

By challenging the classical graph Laplacian, we propose a new convolution operator that is provably robust in the spectral domain and is incorporated in the GCN architecture to improve expressivity and interpretability.

Adversarial Robustness

Stability-certified reinforcement learning: A control-theoretic perspective

no code implementations26 Oct 2018 Ming Jin, Javad Lavaei

We investigate the important problem of certifying stability of reinforcement learning policies when interconnected with nonlinear dynamical systems.

reinforcement-learning Reinforcement Learning (RL)

Inverse Reinforcement Learning via Deep Gaussian Process

no code implementations26 Dec 2015 Ming Jin, Andreas Damianou, Pieter Abbeel, Costas Spanos

We propose a new approach to inverse reinforcement learning (IRL) based on the deep Gaussian process (deep GP) model, which is capable of learning complicated reward structures with few demonstrations.

reinforcement-learning Reinforcement Learning (RL)

Environmental Sensing by Wearable Device for Indoor Activity and Location Estimation

no code implementations22 Jun 2014 Ming Jin, Han Zou, Kevin Weekly, Ruoxi Jia, Alexandre M. Bayen, Costas J. Spanos

We present results from a set of experiments in this pilot study to investigate the causal influence of user activity on various environmental parameters monitored by occupant carried multi-purpose sensors.

energy management Management +1

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